Internet of Things-Based Induction Motor Diagnosis Using Convolutional Neural Network

Abstract

We experimented analyzing motor vibration with aid of Raspberry Pi when, at that time, the engine vibration was abnormal. The Pi signal is transmitted to a relay by the motor supply disconnection. The control unit, nevertheless, monitors and sends the data to the storage system in good form with proper temperature. A FO-PID controller is utilized to analyze the effects of IM due to harmonic current, vibration, and noise. The induction motor’s response to harmonic and current fluctuations is stabilized by a FO-PID controller. The findings can be displayed on the mobile. The tests were carried out in a static state of vibration condition, and fast Fourier transformation is used to analyze the measured vibration data signals. The results of this model were based on the convolutional neural network (CNN), which considerably monitors early diagnostics of the vibration. With a maximum delay of around 1 s, the controller can forward cloud vibration data. Using the CNN model train to analyze the performance of the classification accuracy, the stored data are collected. This article offers a novel way of building tools for measuring vibration in real time based on the schematic architecture provided by the Python mode.

Keywords:

• induction motor
• vibration sensor
• micro-electromechanical systems sensor
• convolution neural network
• fast Fourier transform

Data Availability Statement

Data sharing not applicable to this article as no datasets were generated or analyzed during the current study

Disclosure Statement

Conflict of interest is not applicable in this work.

Ethics Approval and Consent to Participate

No participation of humans takes place in this implementation process

Human and Animal Rights

No violation of Human and Animal Rights is involved.

Additional information

Notes on contributors

Mubaraali L

L. Mubaraali presently working as Assistant Professor, Department of ECE, SNS College of Engineering, Sarvanampatty, Coimbatore. He received his B.E Degree in Electronics and Communication Engg. from Maharaja Prithivi Engineering College, Coimbatore, M.E., in VLSI DESIGN. From Regional centre Anna University, Coimbatore and Pursuing Ph.D in Information & Communication Engineering at Anna University, Chennai.

Kuppuswamy N

N. Kuppuswamy presently is working as Professor in Department of Mechanical Engineering KIT- Kalaignar Karunanidhi Institute of Technology in Coimbatore. He received his B.E Degree in Mechanical Engineering, PSG College of Technology, Coimbatore, M.E., in Production Engineering from PSG College of Technology, Coimbatore and completed Ph.D in Production Engineering from PSG College of Technology, in 2005.

Muthukumar R

R. Muthukumar presently working as Associate professor in Erode Sengunthar Engineering College, Erode. He received his B.E Degree in Electrical and Electronics Engg. from CIT, Coimbatore, M.E., in Power Systems Engg. From GCT, Coimbatore and completed Ph. D in Power System Engineering at Anna University, Chennai, in 2014. He has published more than eighteen international journals and has fifteen International/National conference publications. His research interest includes power system planning, voltage stability analysis and application of evolutionary algorithms to power system optimization.